Progress in building a machine that can ask interesting and informative questions

Anselm Rothe, New York University

Brenden Lake, New York University

Todd Gureckis, New York University

Abstract

Asking creative questions is a hallmark of human cognition. In
comparison, machine learning systems that attempt to mimic this ability are still
extremely limited (e.g., current chatbots ask questions based on preprogrammed
routines). In the present work, we developed a computational model of question
generation. Based on a corpus of questions collected from online participants
playing an information-seeking game, we designed a “grammar of
questions.” The grammar is powerful enough to represent all human
questions we collected and thus defines the “question space.” Given
a particular context (game scenario), people are more likely to ask (generate)
some questions that others. Our computational model predicts these likelihoods,
that is, a probability distribution over the question space. In addition, the
model can generalize to novel contexts. Key model ingredients are informativity,
compositionality, and length of a question.